Various modern media measurement and analytics solutions are focused on collecting data based on only one source. Generically the sources are so-called panel studies where user behaviors are metered either through dedicated devices or downloadable software meters, or through embedded tags (on (web) sites or apps (applications)) or SDKs (apps) that collect data on a particular app. Alternatively, desired data may be acquired through traditional user survey studies or interviews which suffer from the problem of respondent subjectivity and inaccuracy.
In the aforementioned studies the evident goal is to get a grasp of the underlying trends, habits, problems and needs of users. However, each of the current methods has its own underlying problems. For example, with few exceptions, the costs of recruiting, maintaining, and validating a panel that is representative behaviorally and demographically, are prohibitive. SDKs (software development kit) and tags only provide data on participating properties but not all. Surveys and interviews are a better indication of brand strength than actual behavior and there is no existing approach that could provide information conceptually on all key areas of the Internet ecosystem in terms of ‘hard’, objective, observation-driven data: hardware installed base and sales, content and app distribution, and usage/transactions as completed by the user.
The evolution of media and Internet services such as web sites or web-accessible services is now faster than ever, and new devices emerge in the market place continuously. Also, one user typically has multiple, rather than one, Internet-capable devices. Holistic understanding of not only usage, but also devices and content distribution, would be needed to explain the market dynamics and to provide all-in-one research products to key customers participating in the Internet ecosystem.
As an example, penetration of certain type of devices affects the distribution of a certain service, either because it is pre-embedded in the device or the app stores (or other content distribution mechanisms) are driving the downloads of that service with that particular device. As another example, it is not enough to understand how many people download certain apps from app stores, but more increasingly it is important to understand the conversion from downloads to actual usage, and further to the money spent by the user in using the service. Further, the popularity of and variety of services available on a certain device can affect the sales of that device due to better perceived functionality, user experience, or through social circles. There are therefore significant feedback loops in this system.
Another prevalent trend is that people not only have multiple devices, but they also use multiple user interfaces, wearable technologies, or attached devices, all working totally or partially supported by a so-called master device. Tangible examples include smartwatches or digital goggles type of devices, which are further attached to a smartphone device. The measurement of activities through those attached devices and wearables, is certainly of future key importance, too.
There's thus a need for scalable media measurement solution capable of observational Internet measurements that are better adapted to the modern media environment where complexity and fragmentation of devices, applications, and services have become the norm. Such a media measurement solution would therefore provide a dynamic, high-resolution approach for holistic Internet metering and analytics, integrating metrics around hardware installed base, content distribution, and user behaviors into a single framework, and leveraging best-in-class methods to capture each facet of the online ecosystem. The result yields an integrated and responsive system capable of providing much more than the sum of its parts, and far more than previous isolated solutions.